jing 2008 
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
tent and the 
key factor for 
it when image 
of gradient is 
; grey-level is 
i>bert, Prewitt, 
or calculating 
bel arithmetic, 
(8) 
Hows: 
(9) 
dded together, 
hole image is 
the change of 
; gradient can 
experiment is 
:ions of image 
tion matching 
lation between 
al information 
rom 4.0 e+005 
»tained, shown 
magnitude has 
no strong relation to the success-matching rate for mutual 
information based method. Even if there are much less features in 
the image, good matching result can also be obtained using mutual 
information matching approach. 
Table 2. The relationship between image gradient and matching 
success rate 
Image 
gradient 
Number of 
input images 
Number of 
correct matching 
Success rate 
<%) 
4.0—4.2 
20 
11 
55.00 
4.2—4.4 
27 
14 
51.85 
4.4—4.6 
34 
25 
73.53 
OO 
1 
33 
22 
66.67 
O 
1 
OO 
30 
23 
76.67 
5.0—5.2 
31 
22 
70.97 
5.2—5.4 
19 
17 
89.47 
5.4—5.6 
28 
26 
92.86 
Os 
1 
bo 
26 
24 
66.67 
5.8—6.0 
19 
16 
84.21 
4.3 Self-Similar Pattern 
In image matching, self-similar pattern in reference image 
seriously affects success rate of matching. Self-similar pattern 
often indicates some sub-area, which grey or some features appear 
in the reference image repeatedly. Different method has different 
definition about self-similar pattern. For area-based method, the 
definition of self-similar pattern is the number of sub-areas, which 
have similar grey level distribution. Whereas, for feature-based 
methods, the definition of self-similar pattern is the number of sub- 
areas, which have similar feature distribution (Xie, et al., 1997). 
Suppose a sub-image i is selected from reference image, its self 
similar pattern is defined as follows: 
cfi=^~ (10) 
Where 5 is the number of all sub-images, which are used for 
matching when sub-image i is searching on the reference image 
pixel by pixel. Suppose the size of a reference image is MxN 
pixels, and the size of a sub-image is mxn pixels, then, 
s = (M - m +1) x (TV - n +1), where Pj is the number of sub-images 
in 5 , whose grey correlation coefficient obtained by matching 
them to sub-image i is greater than the threshold TH . 
It is known that c/ ( denotes two dimensions relativity about a sub- 
area to the whole area. If a number of sub-images are cropped 
equably from reference image, then, the mean self-similar pattern 
value of these sub-images can be used to inspect two dimensions 
relativity of reference image. Therefore, self-similar pattern value 
of a reference image is defined as the following equation: 
i=1 
Where / is the number of sub-images cropped from the reference 
image. And these sub-images must satisfy some requirements. 
The bigger the self-similar pattern value is, the stronger two 
dimensions relativity of image self is, and the higher the error of 
matching is. In the calculating of self-similar pattern value, TH 
and n are two important parameters. Selection of TH is a key 
problem. Only when a reasonable TH is selected, characteristics 
of image’s self-matching can behave fully. The Selection of n 
affects the calculation time and reliability of self-similar pattern 
value. Sampling interval determines how much n is. Setting 
TH = 0.96 and n = 20 are made after experiments in the paper. 
The relationship between self-similar pattern and success rate of 
matching based on mutual information is shown in Table 3 and 
Figure 5. 
Table 3. The relationship between self-similar pattern value for 
different scene and success rate of matching 
Different scene 
image 
Self-similar pattern 
value 
Success rate of 
matching (%) 
Farmland 
0.989 
69 
Village 
0.519 
78 
River 
0.336 
81 
Town 
0.117 
93 
Stream 
0.010 
100 
Self-similar pattem value 
Figure 5. Self-similar pattern and success rate of image matching 
From Figure 5 we can see that the bigger the value of self-similar 
pattern is, the lower the success rate of matching is, since there are 
many self-similar areas in the reference image, which leads to 
many mistakes in matching and reduce the validity of matching 
based on MI. Self-similar pattern means how many self-similar 
areas are in one image, the more the self-similar areas there are, 
the more the peaks of MI value there are, and local maximum of 
MI will cause matching error. There is a strong relativity between 
matching success rate and self-similar pattern. 
4.4 The Validity of Mutual Information Similarity Metric 
The validity of mutual information similarity metric applied in 
template matching for dissimilar image can be validated by success 
rate of matching. The success rate of matching shows the ability of 
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